This paper focuses on the reduced basis method in the case of non-linear and non-affinely parametrized partial differential equations where affine decomposition is not obtained. In this context, Empirical Interpolation Method (EIM) is commonly used to recover the affine decomposition necessary to deploy the Reduced Basis (RB) methodology. The build of each EIM approximation requires many finite element solves which increases significantly the computational cost hence making it inefficient on large problems. We propose a Simultaneous EIM and RB method (SER) whose principle is based on the use of reduced basis approximations into the EIM building step. The number of finite element solves required by SER can drop to N + 1 where N is the dimension of the RB approximation space, thus providing a huge computational gain. The SER method has already been introduced and illustrated on a 2D benchmark. This paper develops the SER method with some variants and in particular a multilevel SER, SER(l) which improves significantly SER at the cost of lN + 1 finite element solves. Finally we discuss these extensions on a 3D multi-physics problem.
Read the full article at: hal.archives-ouvertes.fr
A new paper on our algorithm SER, with C. Daversin, that enables the reduced basis method for non-linear multi-physics problems at a reduced cost. The cost can drop to N+1 finite element solves where N is the number of reduced basis functions. It has been submitted for review.